Reddit Sentiment Analysis for Apple WWDC25
Scrapes Reddit for Apple WWDC25 discussions, classifies topics, analyzes comment sentiment with Gemini, and logs results to a Google Sheet.
What it does
Track Apple WWDC25 discussions on Reddit by automatically scraping posts with Bright Data and classifying topics using a Text Classifier. Analyze the sentiment of every comment with Google Gemini to gauge public reaction, then log all results directly into your Google Sheet for instant brand monitoring and product feedback insights.
Step by step
- Configure the Bright Data scraper with specific Reddit URLs and filters for WWDC25 keywords to extract raw post data.
- Set up the HTTP Request node to pass the scraped JSON payload directly into the Text Classifier without modification.
- Define the Google Sheets append action to map the final sentiment score and category labels to specific column headers.
Where the LLM does the work
- Prompt the Google Gemini Chat Model with a system instruction that defines nuanced sentiment categories beyond simple positive or negative.
- Instruct the LLM to reason through ambiguous comments where sarcasm or technical jargon might skew the initial classification.
- Generate dynamic summary insights from the aggregated sheet data to highlight emerging trends during the WWDC25 keynote.
Watch out for
Sentiment analysis can misinterpret technical praise as negative sentiment if the prompt lacks context about Apple's engineering culture.
Tools that fit
HTTP Request API Executing API calls to Bright Data
Google Gemini Chat Model LLM Performing sentiment analysis on comments
Text Classifier Service Categorizing posts and comments
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Reddit Sentiment Analysis for Apple WWDC25: Scrapes Reddit for Apple WWDC25 discussions, classifies topics, analyzes comment sentiment with Gemini, and logs results to a Google Sheet. ## Specification - What it does: Track Apple WWDC25 discussions on Reddit by automatically scraping posts with Bright Data and classifying topics using a Text Classifier. Analyze the sentiment of every comment with Google Gemini to gauge public reaction, then log all results directly into your Google Sheet for instant brand monitoring and product feedback insights. - Trigger: Run manually (Manual · on demand) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Bright Data: Scraping Reddit data - HTTP Request: Executing API calls to Bright Data - Google Gemini Chat Model: Performing sentiment analysis on comments - Text Classifier: Categorizing posts and comments - Google Sheets: Storing analysis results ## Known pitfalls, handle each one explicitly in your implementation 1. Sentiment analysis can misinterpret technical praise as negative sentiment if the prompt lacks context about Apple's engineering culture. ## Reference implementation https://n8n.io/workflows/4980 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/reddit-sentiment-analysis-for-apple-wwdc25/ via usecasesforagents.com
Frequently asked questions
Can I use a different Web Scraping than Bright Data?
Yes. Bright Data is only the example web scraping in this recipe. The same flow works with Airtop, Apify, Decodo and Firecrawl. Swap the web scraping connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for marketers who'd rather brief an agent than start from a blank doc.
Analysts get a ready-made pattern for turning raw numbers into a report someone will actually read.